

import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin





class       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							None
			SCREAMING_SNAKE_CASE	:  int		     =							None







			@property
			def 							snake_case__(	self					:							Optional[Any]				) ->int:
									return self.feat_extract_tester.prepare_feat_extract_dict()







			def 							snake_case__(	self					:							int				) ->Optional[Any]:
									snake_case_  =      self.feature_extraction_class(**self.feat_extract_dict				)
									self.assertTrue(hasattr(_UpperCamelCase				,  '''feature_size'''				)				)
									self.assertTrue(hasattr(_UpperCamelCase				,  '''sampling_rate'''				)				)
									self.assertTrue(hasattr(_UpperCamelCase				,  '''padding_value'''				)				)







			def 							snake_case__(	self					:							Tuple				) ->List[str]:
									snake_case_  =      self.feat_extract_tester.prepare_inputs_for_common()
									snake_case_  =      self.feature_extraction_class(**self.feat_extract_dict				)
									snake_case_  =      feat_extract.model_input_names[0]

									snake_case_  =      BatchFeature({input_name: speech_inputs}				)

									self.assertTrue(all(len(_UpperCamelCase				) == len(_UpperCamelCase				) for x, y in zip(_UpperCamelCase				,  processed_features[input_name]				)				)				)

									snake_case_  =      self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCamelCase				)
									snake_case_  =      BatchFeature({input_name: speech_inputs}				,  tensor_type='''np'''				)

									snake_case_  =      processed_features[input_name]

									if len(batch_features_input.shape				) < 3:
															snake_case_  =      batch_features_input[:, :, None]

									self.assertTrue(
									    batch_features_input.shape
									    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)







			@require_torch
			def 							snake_case__(	self					:							Optional[Any]				) ->Union[str, Any]:
									snake_case_  =      self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCamelCase				)
									snake_case_  =      self.feature_extraction_class(**self.feat_extract_dict				)
									snake_case_  =      feat_extract.model_input_names[0]

									snake_case_  =      BatchFeature({input_name: speech_inputs}				,  tensor_type='''pt'''				)

									snake_case_  =      processed_features[input_name]

									if len(batch_features_input.shape				) < 3:
															snake_case_  =      batch_features_input[:, :, None]

									self.assertTrue(
									    batch_features_input.shape
									    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)







			@require_tf
			def 							snake_case__(	self					:							Tuple				) ->str:
									snake_case_  =      self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCamelCase				)
									snake_case_  =      self.feature_extraction_class(**self.feat_extract_dict				)
									snake_case_  =      feat_extract.model_input_names[0]

									snake_case_  =      BatchFeature({input_name: speech_inputs}				,  tensor_type='''tf'''				)

									snake_case_  =      processed_features[input_name]

									if len(batch_features_input.shape				) < 3:
															snake_case_  =      batch_features_input[:, :, None]

									self.assertTrue(
									    batch_features_input.shape
									    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)







			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							Any=False				) ->List[Any]:
									def _inputs_have_equal_length(_UpperCamelCase					:							Dict				):
															snake_case_  =      len(input[0]				)
															for input_slice in input[1:]:
																					if len(_UpperCamelCase				) != length:
																											return False
															return True

									def _inputs_are_equal(_UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							Optional[int]				):
															if len(_UpperCamelCase				) != len(_UpperCamelCase				):
																					return False

															for input_slice_a, input_slice_a in zip(_UpperCamelCase				,  _UpperCamelCase				):
																					if not np.allclose(np.asarray(_UpperCamelCase				)				,  np.asarray(_UpperCamelCase				)				,  atol=1e-3				):
																											return False
															return True

									snake_case_  =      self.feature_extraction_class(**self.feat_extract_dict				)
									snake_case_  =      self.feat_extract_tester.prepare_inputs_for_common(numpify=_UpperCamelCase				)
									snake_case_  =      feat_extract.model_input_names[0]

									snake_case_  =      BatchFeature({input_name: speech_inputs}				)

									snake_case_  =      self.feat_extract_tester.seq_length_diff
									snake_case_  =      self.feat_extract_tester.max_seq_length + pad_diff
									snake_case_  =      self.feat_extract_tester.min_seq_length
									snake_case_  =      self.feat_extract_tester.batch_size
									snake_case_  =      self.feat_extract_tester.feature_size

									# test padding for List[int] + numpy
									snake_case_  =      feat_extract.pad(_UpperCamelCase				,  padding=_UpperCamelCase				)
									snake_case_  =      input_a[input_name]

									snake_case_  =      feat_extract.pad(_UpperCamelCase				,  padding='''longest'''				)
									snake_case_  =      input_a[input_name]

									snake_case_  =      feat_extract.pad(_UpperCamelCase				,  padding='''max_length'''				,  max_length=len(speech_inputs[-1]				)				)
									snake_case_  =      input_a[input_name]

									snake_case_  =      feat_extract.pad(_UpperCamelCase				,  padding='''longest'''				,  return_tensors='''np'''				)
									snake_case_  =      input_a[input_name]

									# max_length parameter has to be provided when setting `padding="max_length"`
									with self.assertRaises(_UpperCamelCase				):
															feat_extract.pad(_UpperCamelCase				,  padding='''max_length'''				)[input_name]

									snake_case_  =      feat_extract.pad(
									    _UpperCamelCase				,  padding='''max_length'''				,  max_length=_UpperCamelCase				,  return_tensors='''np'''				)
									snake_case_  =      input_a[input_name]

									self.assertFalse(_inputs_have_equal_length(_UpperCamelCase				)				)
									self.assertTrue(_inputs_have_equal_length(_UpperCamelCase				)				)
									self.assertTrue(_inputs_have_equal_length(_UpperCamelCase				)				)
									self.assertTrue(_inputs_are_equal(_UpperCamelCase				,  _UpperCamelCase				)				)
									self.assertTrue(len(input_a[0]				) == pad_min_length				)
									self.assertTrue(len(input_a[1]				) == pad_min_length + pad_diff				)
									self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]				))				)
									self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)				)

									if feature_size > 1:
															self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size				)

									# test padding for `pad_to_multiple_of` for List[int] + numpy
									snake_case_  =      feat_extract.pad(_UpperCamelCase				,  pad_to_multiple_of=1_0				)
									snake_case_  =      input_a[input_name]

									snake_case_  =      feat_extract.pad(_UpperCamelCase				,  padding='''longest'''				,  pad_to_multiple_of=1_0				)
									snake_case_  =      input_a[input_name]

									snake_case_  =      feat_extract.pad(
									    _UpperCamelCase				,  padding='''max_length'''				,  pad_to_multiple_of=1_0				,  max_length=_UpperCamelCase				)
									snake_case_  =      input_a[input_name]

									snake_case_  =      feat_extract.pad(
									    _UpperCamelCase				,  padding='''max_length'''				,  pad_to_multiple_of=1_0				,  max_length=_UpperCamelCase				,  return_tensors='''np'''				,  )
									snake_case_  =      input_a[input_name]

									self.assertTrue(all(len(_UpperCamelCase				) % 1_0 == 0 for x in input_a				)				)
									self.assertTrue(_inputs_are_equal(_UpperCamelCase				,  _UpperCamelCase				)				)

									snake_case_  =      pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
									self.assertTrue(all(len(_UpperCamelCase				) == expected_mult_pad_length for x in input_a				)				)
									self.assertEqual(input_a.shape[:2]				,  (batch_size, expected_mult_pad_length)				)

									if feature_size > 1:
															self.assertTrue(input_a.shape[2] == feature_size				)

									# Check padding value is correct
									snake_case_  =      (np.ones(self.feat_extract_tester.feature_size				) * feat_extract.padding_value).sum()
									self.assertTrue(
									    abs(np.asarray(input_a[0]				)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)				)
									    < 1e-3				)
									self.assertTrue(
									    abs(
									        np.asarray(input_a[1]				)[pad_min_length + pad_diff :].sum()
									        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)				)
									    < 1e-3				)
									self.assertTrue(
									    abs(
									        np.asarray(input_a[2]				)[pad_min_length + 2 * pad_diff :].sum()
									        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)				)
									    < 1e-3				)
									self.assertTrue(
									    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)				) < 1e-3				)
									self.assertTrue(
									    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)				)
									    < 1e-3				)







			def 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							str=False				) ->List[Any]:
									def _inputs_have_equal_length(_UpperCamelCase					:							Any				):
															snake_case_  =      len(input[0]				)
															for input_slice in input[1:]:
																					if len(_UpperCamelCase				) != length:
																											return False
															return True

									def _inputs_are_equal(_UpperCamelCase					:							Any				,  _UpperCamelCase					:							List[Any]				):
															if len(_UpperCamelCase				) != len(_UpperCamelCase				):
																					return False

															for input_slice_a, input_slice_a in zip(_UpperCamelCase				,  _UpperCamelCase				):
																					if not np.allclose(np.asarray(_UpperCamelCase				)				,  np.asarray(_UpperCamelCase				)				,  atol=1e-3				):
																											return False
															return True

									snake_case_  =      self.feature_extraction_class(**self.feat_extract_dict				)
									snake_case_  =      self.feat_extract_tester.prepare_inputs_for_common(numpify=_UpperCamelCase				)
									snake_case_  =      feat_extract.model_input_names[0]

									snake_case_  =      BatchFeature({input_name: speech_inputs}				)

									# truncate to smallest
									snake_case_  =      feat_extract.pad(
									    _UpperCamelCase				,  padding='''max_length'''				,  max_length=len(speech_inputs[0]				)				,  truncation=_UpperCamelCase				)
									snake_case_  =      input_a[input_name]

									snake_case_  =      feat_extract.pad(_UpperCamelCase				,  padding='''max_length'''				,  max_length=len(speech_inputs[0]				)				)
									snake_case_  =      input_a[input_name]

									self.assertTrue(_inputs_have_equal_length(_UpperCamelCase				)				)
									self.assertFalse(_inputs_have_equal_length(_UpperCamelCase				)				)

									# truncate to smallest with np
									snake_case_  =      feat_extract.pad(
									    _UpperCamelCase				,  padding='''max_length'''				,  max_length=len(speech_inputs[0]				)				,  return_tensors='''np'''				,  truncation=_UpperCamelCase				,  )
									snake_case_  =      input_a[input_name]

									snake_case_  =      feat_extract.pad(
									    _UpperCamelCase				,  padding='''max_length'''				,  max_length=len(speech_inputs[0]				)				,  return_tensors='''np'''				)
									snake_case_  =      input_a[input_name]

									self.assertTrue(_inputs_have_equal_length(_UpperCamelCase				)				)
									self.assertTrue(input_a.shape[1] == len(speech_inputs[0]				)				)

									# since truncation forces padding to be smaller than longest input
									# function can't return `np.ndarray`, but has to return list
									self.assertFalse(_inputs_have_equal_length(_UpperCamelCase				)				)

									# truncate to middle
									snake_case_  =      feat_extract.pad(
									    _UpperCamelCase				,  padding='''max_length'''				,  max_length=len(speech_inputs[1]				)				,  truncation=_UpperCamelCase				,  return_tensors='''np'''				,  )
									snake_case_  =      input_a[input_name]

									snake_case_  =      feat_extract.pad(
									    _UpperCamelCase				,  padding='''max_length'''				,  max_length=len(speech_inputs[1]				)				,  truncation=_UpperCamelCase				)
									snake_case_  =      input_a[input_name]

									snake_case_  =      feat_extract.pad(
									    _UpperCamelCase				,  padding='''max_length'''				,  max_length=len(speech_inputs[1]				)				,  return_tensors='''np'''				)
									snake_case_  =      input_a[input_name]

									self.assertTrue(input_a.shape[1] == len(speech_inputs[1]				)				)
									self.assertTrue(_inputs_have_equal_length(_UpperCamelCase				)				)
									self.assertTrue(_inputs_have_equal_length(_UpperCamelCase				)				)
									self.assertTrue(_inputs_are_equal(_UpperCamelCase				,  _UpperCamelCase				)				)

									# since truncation forces padding to be smaller than longest input
									# function can't return `np.ndarray`, but has to return list
									self.assertFalse(_inputs_have_equal_length(_UpperCamelCase				)				)
									self.assertTrue(len(input_a[-1]				) == len(speech_inputs[-1]				)				)

									# padding has to be max_length when setting `truncation=True`
									with self.assertRaises(_UpperCamelCase				):
															feat_extract.pad(_UpperCamelCase				,  truncation=_UpperCamelCase				)[input_name]

									# padding has to be max_length when setting `truncation=True`
									with self.assertRaises(_UpperCamelCase				):
															feat_extract.pad(_UpperCamelCase				,  padding='''longest'''				,  truncation=_UpperCamelCase				)[input_name]

									# padding has to be max_length when setting `truncation=True`
									with self.assertRaises(_UpperCamelCase				):
															feat_extract.pad(_UpperCamelCase				,  padding='''longest'''				,  truncation=_UpperCamelCase				)[input_name]

									# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
									with self.assertRaises(_UpperCamelCase				):
															feat_extract.pad(_UpperCamelCase				,  padding='''max_length'''				,  truncation=_UpperCamelCase				)[input_name]

									# test truncation for `pad_to_multiple_of` for List[int] + numpy
									snake_case_  =      1_2
									snake_case_  =      feat_extract.pad(
									    _UpperCamelCase				,  padding='''max_length'''				,  max_length=len(speech_inputs[0]				)				,  pad_to_multiple_of=_UpperCamelCase				,  truncation=_UpperCamelCase				,  )
									snake_case_  =      input_a[input_name]

									snake_case_  =      feat_extract.pad(
									    _UpperCamelCase				,  padding='''max_length'''				,  max_length=len(speech_inputs[0]				)				,  pad_to_multiple_of=_UpperCamelCase				,  )
									snake_case_  =      input_a[input_name]

									# retrieve expected_length as multiple of pad_to_multiple_of
									snake_case_  =      len(speech_inputs[0]				)
									if expected_length % pad_to_multiple_of != 0:
															snake_case_  =      ((len(speech_inputs[0]				) // pad_to_multiple_of) + 1) * pad_to_multiple_of

									self.assertTrue(len(input_a[0]				) == expected_length				)
									self.assertTrue(_inputs_have_equal_length(_UpperCamelCase				)				)
									self.assertFalse(_inputs_have_equal_length(_UpperCamelCase				)				)







			def 							snake_case__(	self					:							Dict				) ->Optional[int]:
									self._check_padding(numpify=_UpperCamelCase				)







			def 							snake_case__(	self					:							Dict				) ->int:
									self._check_padding(numpify=_UpperCamelCase				)







			def 							snake_case__(	self					:							Dict				) ->List[Any]:
									self._check_truncation(numpify=_UpperCamelCase				)







			def 							snake_case__(	self					:							Tuple				) ->List[Any]:
									self._check_truncation(numpify=_UpperCamelCase				)







			@require_torch
			def 							snake_case__(	self					:							Union[str, Any]				) ->Any:
									snake_case_  =      self.feature_extraction_class(**self.feat_extract_dict				)
									snake_case_  =      self.feat_extract_tester.prepare_inputs_for_common()
									snake_case_  =      feat_extract.model_input_names[0]

									snake_case_  =      BatchFeature({input_name: speech_inputs}				)

									snake_case_  =      feat_extract.pad(_UpperCamelCase				,  padding='''longest'''				,  return_tensors='''np'''				)[input_name]
									snake_case_  =      feat_extract.pad(_UpperCamelCase				,  padding='''longest'''				,  return_tensors='''pt'''				)[input_name]

									self.assertTrue(abs(input_np.astype(np.floataa				).sum() - input_pt.numpy().astype(np.floataa				).sum()				) < 1e-2				)







			@require_tf
			def 							snake_case__(	self					:							int				) ->Union[str, Any]:
									snake_case_  =      self.feature_extraction_class(**self.feat_extract_dict				)
									snake_case_  =      self.feat_extract_tester.prepare_inputs_for_common()
									snake_case_  =      feat_extract.model_input_names[0]

									snake_case_  =      BatchFeature({input_name: speech_inputs}				)

									snake_case_  =      feat_extract.pad(_UpperCamelCase				,  padding='''longest'''				,  return_tensors='''np'''				)[input_name]
									snake_case_  =      feat_extract.pad(_UpperCamelCase				,  padding='''longest'''				,  return_tensors='''tf'''				)[input_name]

									self.assertTrue(abs(input_np.astype(np.floataa				).sum() - input_tf.numpy().astype(np.floataa				).sum()				) < 1e-2				)







			def 							snake_case__(	self					:							Tuple				) ->Optional[int]:
									snake_case_  =      self.feat_extract_dict
									snake_case_  =      True
									snake_case_  =      self.feature_extraction_class(**_UpperCamelCase				)
									snake_case_  =      self.feat_extract_tester.prepare_inputs_for_common()
									snake_case_  =      [len(_UpperCamelCase				) for x in speech_inputs]
									snake_case_  =      feat_extract.model_input_names[0]

									snake_case_  =      BatchFeature({input_name: speech_inputs}				)

									snake_case_  =      feat_extract.pad(_UpperCamelCase				,  padding='''longest'''				,  return_tensors='''np'''				)
									self.assertIn('''attention_mask'''				,  _UpperCamelCase				)
									self.assertListEqual(list(processed.attention_mask.shape				)				,  list(processed[input_name].shape[:2]				)				)
									self.assertListEqual(processed.attention_mask.sum(-1				).tolist()				,  _UpperCamelCase				)







			def 							snake_case__(	self					:							List[str]				) ->Optional[int]:
									snake_case_  =      self.feat_extract_dict
									snake_case_  =      True
									snake_case_  =      self.feature_extraction_class(**_UpperCamelCase				)
									snake_case_  =      self.feat_extract_tester.prepare_inputs_for_common()
									snake_case_  =      [len(_UpperCamelCase				) for x in speech_inputs]
									snake_case_  =      feat_extract.model_input_names[0]

									snake_case_  =      BatchFeature({input_name: speech_inputs}				)
									snake_case_  =      min(_UpperCamelCase				)

									snake_case_  =      feat_extract.pad(
									    _UpperCamelCase				,  padding='''max_length'''				,  max_length=_UpperCamelCase				,  truncation=_UpperCamelCase				,  return_tensors='''np'''				)
									self.assertIn('''attention_mask'''				,  _UpperCamelCase				)
									self.assertListEqual(
									    list(processed_pad.attention_mask.shape				)				,  [processed_pad[input_name].shape[0], max_length]				)
									self.assertListEqual(
									    processed_pad.attention_mask[:, :max_length].sum(-1				).tolist()				,  [max_length for x in speech_inputs]				)

import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
    import bsa
    from bsa import BeautifulSoup


__lowerCAmelCase     :	int       			=  logging.get_logger(__name__)




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def __init__(					self     :					Union[str, Any]     ,				**UpperCamelCase__     :					Union[str, Any]       )     ->      Union[str, Any]:







    """simple docstring"""






    requires_backends(self     ,				["""bs4"""]       )
    super().__init__(**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]
    __magic_name__								=			element if element.name else element.parent
    for parent in child.parents:  # type: bs4.element.Tag
      __magic_name__								=			parent.find_all(child.name     ,				recursive=UpperCamelCase__       )
      xpath_tags.append(child.name       )
      xpath_subscripts.append(
          0 if 1 == len(UpperCamelCase__       ) else next(i for i, s in enumerate(UpperCamelCase__     ,				1       ) if s is child       )       )
      __magic_name__								=			parent
    xpath_tags.reverse()
    xpath_subscripts.reverse()
    return xpath_tags, xpath_subscripts
  def 				_lowercase				(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			BeautifulSoup(UpperCamelCase__     ,				"""html.parser"""       )

    __magic_name__								=			[]
    __magic_name__								=			[]
    __magic_name__								=			[]

    for element in html_code.descendants:
      if type(UpperCamelCase__       ) == bsa.element.NavigableString:
        if type(element.parent       ) != bsa.element.Tag:
          continue

        __magic_name__								=			html.unescape(UpperCamelCase__       ).strip()
        if not text_in_this_tag:
          continue

        all_doc_strings.append(UpperCamelCase__       )

        __magic_name__	,       __magic_name__								=			self.xpath_soup(UpperCamelCase__       )
        stringaxtag_seq.append(UpperCamelCase__       )
        stringaxsubs_seq.append(UpperCamelCase__       )

    if len(UpperCamelCase__       ) != len(UpperCamelCase__       ):
      raise ValueError("""Number of doc strings and xtags does not correspond"""       )
    if len(UpperCamelCase__       ) != len(UpperCamelCase__       ):
      raise ValueError("""Number of doc strings and xsubs does not correspond"""       )

    return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			""""""
    for tagname, subs in zip(UpperCamelCase__     ,				UpperCamelCase__       ):
      xpath += F'''/{tagname}'''
      if subs != 0:
        xpath += F'''[{subs}]'''
    return xpath





  def __call__(					self     :					str     ,				UpperCamelCase__     :					Tuple       )     ->      BatchFeature:







    """simple docstring"""






    __magic_name__								=			False

    # Check that strings has a valid type
    if isinstance(UpperCamelCase__     ,				UpperCamelCase__       ):
      __magic_name__								=			True
    elif isinstance(UpperCamelCase__     ,				(list, tuple)       ):
      if len(UpperCamelCase__       ) == 0 or isinstance(html_strings[0]     ,				UpperCamelCase__       ):
        __magic_name__								=			True

    if not valid_strings:
      raise ValueError(
          """HTML strings must of type `str`, `List[str]` (batch of examples), """
          F'''but is of type {type(UpperCamelCase__       )}.'''       )

    __magic_name__								=			bool(isinstance(UpperCamelCase__     ,				(list, tuple)       ) and (isinstance(html_strings[0]     ,				UpperCamelCase__       ))       )

    if not is_batched:
      __magic_name__								=			[html_strings]

    # Get nodes + xpaths
    __magic_name__								=			[]
    __magic_name__								=			[]
    for html_string in html_strings:
      __magic_name__	,       __magic_name__	,       __magic_name__								=			self.get_three_from_single(UpperCamelCase__       )
      nodes.append(UpperCamelCase__       )
      __magic_name__								=			[]
      for node, tag_list, sub_list in zip(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       ):
        __magic_name__								=			self.construct_xpath(UpperCamelCase__     ,				UpperCamelCase__       )
        xpath_strings.append(UpperCamelCase__       )
      xpaths.append(UpperCamelCase__       )

    # return as Dict
    __magic_name__								=			{"""nodes""": nodes, """xpaths""": xpaths}
    __magic_name__								=			BatchFeature(data=UpperCamelCase__     ,				tensor_type=UpperCamelCase__       )

    return encoded_inputs

